Clinical assessment revealed a significant relationship between UU and MH infections and male infertility. UU was found to significantly affect sperm quality, but this was not the case with MH. Doxycycline and josamycin should be preferred for clinically treating UU and MH infections.

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Many factors such as trauma and COVID-19 cause acute kidney injury (AKI). Late AKI have a very high incidence and mortality rate. Early diagnosis of AKI provides a critical therapeutic time window for AKI treatment to prevent progression to chronic renal failure. However, the current clinical detection based on creatinine and urine output isn't effective in diagnosing early AKI. In recent years, the early diagnosis of AKI has made great progress with the advancement of information technology, nanotechnology, and biomedicine. These emerging methods are mainly divided into two aspects: First, predicting AKI through models construct by machine learning; Second, early diagnosis of AKI through detection of newly-discovered early biomarkers. Currently, these methods have shown great potential and become an attractive tool for the early diagnosis of AKI. Therefore, it is very important to discuss and summarize these methods for the early diagnosis of AKI. In this review, we first systematically summarize the application of machine learning in AKI prediction algorithms and specific scenarios. In addition, we introduce the key role of early biomarkers in the progress of AKI, and then comprehensively summarize the application of emerging detection technologies for early AKI. Finally, we discuss current challenges and prospects of machine learning and biomarker detection. The review is expected to provide new insights for early diagnosis of AKI, and provided important inspiration for the design of early diagnosis of other major diseases.

We measure the expansion of the forward shock of the Small Magellanic Cloud supernova remnant 1E 0102.2–7219 in X-rays using Chandra X-Ray Observatory on-axis Advanced CCD Imaging Spectrometer observations from 1999 to 2016. We estimate an expansion rate of 0.025% ± 0.006% yr−1 and a blast wave velocity of ( 1.61 ± 0.37 ) × 10 3 km s − 1 . Assuming partial electron–ion equilibration via Coulomb collisions and cooling due to adiabatic expansion, this velocity implies a post-shock electron temperature of 0.84 ± 0.20 keV, which is consistent with the estimate of 0.68 ± 0.05 keV based on the X-ray spectral analysis. We combine the expansion rate with the blast wave and reverse shock radii to generate a grid of one-dimensional models for a range of ejecta masses ( 2 – 6 M ☉ ) to constrain the explosion energy, age, circumstellar density, swept-up mass, and unshocked-ejecta mass. We find acceptable solutions for a constant-density ambient medium and for an r −2 power-law profile (appropriate for a constant progenitor stellar wind). For the constant-density case, we find an age of ∼1700 yr, explosion energies (0.87–2.61) × 1051 erg, ambient densities 0.85–2.54 amu cm−3, swept-up masses 22 – 66 M ☉ , and unshocked-ejecta masses 0.05 – 0.16 M ☉ . For the power-law density profile, we find an age of ∼2600 yr, explosion energies (0.34–1.02) × 1051 erg, densities 0.22 – 0.66 amu cm − 3 at the blast wave, swept-up masses 17 – 52 M ☉ , and unshocked-ejecta masses 0.06 – 0.18 M ☉ . Assuming that the true explosion energy was (0.5–1.5) × 1051 erg, ejecta masses 2 – 3.5 M ☉ are favored for the constant-density case and 3 – 6 M ☉ for the power-law case. The unshocked-ejecta mass estimates are comparable to Fe masses expected in core-collapse supernovae with progenitor mass 15.0 – 40.0 M ☉ , offering a possible explanation for the lack of Fe emission observed in X-rays.